CSIRO Early Research Career (CERC) Postdoctoral Fellowships provide opportunities to scientists and engineers who have completed their doctorate and have less than three years of relevant postdoctoral work experience. These fellowships aim to develop the next generation of future leaders of the innovation system.

Future Science Platforms (FSPs) are a major CSIRO initiative. FSPs are multi-year investments in frontier science that will reinvent and create new industries for Australia.

CSIRO Astronomy and Space Science (CASS) and FSP are joining together to appoint a Postdoctoral Fellow in Machine Learning and Artificial Intelligence (ML/AI) to work with world class scientist to help find the ‘unknown’ in Radio Astronomy Data Sets.As the successful candidate, you will develop machine learning algorithms that can be applied to large data structures and can identify unusual and unexpected sources in the ASKAP and Parkes data sets.

Your duties will include:

Carry out innovative, impactful research of strategic importance to CSIRO that will, where possible, lead to novel and important scientific outcomes.

Recognise and exploit opportunities for innovation and the generation of new theoretical perspectives, and progress opportunities for the further development or creation of new lines of research.

Develop general methods for searching for "known unknowns” (pulsars, fast radio bursts, galaxies, etc.) and “unknown unknowns” (the unexpected) in data sets from the Parkes and ASKAP telescopes.

Implement these methods efficiently on high performance computing systems.

Carry out evaluation of the developed software to demonstrate its competitiveness and fitness for purpose. Taking responsibility for functionality, performance and robustness.

A doctorate (or will shortly satisfy the requirements of a PhD) in a relevant discipline area, such as computing, astrophysics or physics.Please note: To be eligible for this role you must have no more than 3 years (or part time equivalent) of postdoctoral research experience.

A sound history of publication in peer reviewed journals and/or authorship of scientific papers, reports, grant applications or patents.

Solid knowledge of machine learning techniques and proven ability to develop and apply such techniques to complex data sets.

The ability to work effectively as part of a multi-disciplinary, regionally dispersed research team, plus the motivation and discipline to carry our autonomous research.